Data drives all the major decisions in the world today. Every business relies on data to make daily strategic decisions. Every decision from attending to customer needs to gaining competitive advantage is made thanks to data.
As individuals we rely on data for even the most basic daily activities including navigation to and from work as well as for communicating with friends and family. But what happens when the data we rely on to make our daily decisions is bad? It can have a drastic impact on our lives whether it’s a small task like choosing where to eat, or deciding whether or not a candidate for a job is qualified to hire. Relying on bad data can also have a drastic impact on your bottom line.
Bad data is Costly
We know that bad data is costly, but just how costly can it be? IBM estimates that bad data costs the US economy roughly $3.1 trillion dollars each year. That’s a huge number. They also found that 1 in 3 business leaders don’t trust the information they use to make decisions. Not only do they not trust the data they are working with, but there is also a high level of uncertainty into whether or not the data is actually accurate. The same study found that roughly 27% of business leaders were unsure of how much of the data they use is accurate. That’s a high level of doubt in reliable data.
A separate research study from Experian Data found that bad data has a direct impact on the bottom line of nearly 90% of all American companies. Their numbers were similar to the report from IBM that showed that US organizations believe on average that 32% of their data is inaccurate. They found that the average loss from bad data accounted for 12% of the company‘s overall revenue. That’s another huge number that directly impacts the bottom line of a company.
Yet another report from Gartner found that 27% of the data in the Fortune 1000 companies is considered flawed. They defined flawed data as data that is inaccurate, incomplete or duplicated. Their research also shows that poor quality data leads to high costs, high customer turnover rate and excessive expenses.
What can history teach us about bad data?
While these examples show many modern problems with bad data, dealing with bad or misleading data is not anything new. The collection and distribution of bad data has been around for thousands of years. Bad data has bankrupted major companies, started wars and even caused entire civilizations to disappear. Utopia Inc, has curated a list of examples of when bad data has changed history. A few of the more interesting examples on the list:
- In 1999, NASA took a $125 million dollar hit when it lost the Mars Orbiter. It turns out that they engineering team responsible for developing the Orbiter used English units of measurement while NASA used the metric system. The problem here is the data was inconsistent making it a rather costly and disastrous mistake.
- The Enron scandal in 2001 was largely a result of bad data. Enron was once the sixth-largest company in the world. A host of fraudulent data provided to Enron’s shareholders resulted in Enron’s meteoric rise and subsequent crash. An ethical external auditing firm could have prevented this fraud from occurring.
- The 2016 United States Presidential election was also mired with bad data. National polling data used to predict state-by-state Electoral College votes led to the prediction of a Hillary Clinton landslide, a forecast that lead to many American voters to stay home on Election Day.
How to mitigate the risk of bad data
Breaking bad data habits can be tricky. As noted in the aforementioned post, there can often be internal resistance to making data-driven changes within your organization. The best way to mitigate risk is by identifying and fixing potential data errors before they have a negative impact on your business and your bottom line. It is easy to make mistakes with your data making it essential to take action to protect your data before facing the negative consequences that can occur.